Service · LLM Development
On OpenAI, Anthropic & Google — built for production, owned by you.
Copilots, retrieval systems, AI agents, and integrations — on whichever of the three frontier engines your workload needs, benchmarked on your tasks and shipped to your own cloud in 4–8 weeks.
Evals pick the engine, not hype
What we build
From use case to a production-grade LLM system.
Calling a model API is a sprint; everything below is the actual product — the nine offerings we scope, price, and build.
LLM strategy & use-case discovery
A frank readiness assessment that ranks your use cases by ROI and feasibility — including the ones not to build.
Custom LLM application development
Full applications with an LLM at the core — built on your stack and business logic, not a generic wrapper.
RAG & knowledge systems
Retrieval over your documents and product data — grounding measured before launch, every answer citing its source.
AI agents & agentic workflows
Agents that execute multi-step work with scoped tools, staged autonomy, and human approval gates where actions have consequences.
Chatbot & copilot development
Customer-facing assistants and internal copilots wired into your real systems — deflection and adoption targets measured weekly.
LLM integration into existing products
AI features added to software you already run — structured outputs into your APIs, model abstraction so a vendor swap never forces a rebuild.
Workflow & document automation
Extraction, classification, and enrichment at volume — schema-validated outputs with unit economics designed before the first invoice.
LLM-powered data analytics
Natural-language access to your warehouse — governed query generation, row-level security respected, every answer traceable.
Evaluation, optimization & scaling
Golden test sets, regression evals, drift monitoring, and cost tuning — the layer that turns each model release into an upgrade, not a rebuild.
Every offering ships with — the same delivery discipline
All three frontier platforms
One team, fluent in all three engines.
Engine choice is an engineering decision — we benchmark on your workload, not on hype. The strongest architectures often route between all three.
01 / OpenAI
GPT-5.5 · GPT-5.4 · Codex
The most widely adopted platform in enterprise surveys
Frontier capability on a software-update cadence — we build so each release lands as an upgrade, not a rewrite. Customer copilots, audited agents, and high-volume pipelines with mini-tier routing designed in.
Runs in: OpenAI API · Azure OpenAI
02 / Anthropic
Opus 4.8 · Sonnet 4.6 · Haiku 4.5
The engine enterprises trust with long-running agents
A 1M-token context window at standard pricing, excelling at multi-step work that has to explain itself afterward — long-horizon agents with full audit trails, whole-archive document intelligence in a single pass.
Runs in: Claude API · Bedrock · Vertex · Foundry
03 / Google
Gemini 3 · 3.5 Flash · 3.5 Pro
Multimodal depth, built where your data already lives
Reads text, audio, images, video, PDFs, and code in one model — up to 2M tokens of context. Native video and audio intelligence, BigQuery-grounded analytics under your IAM, Flash-economics document processing at volume.
Runs in: Your GCP project · Vertex AI
Independence note. We hold no partnership, reseller, or referral relationship with OpenAI, Anthropic, or Google. The recommendation you get is the one your evaluation results earn; nobody pays us to steer it.
Of corporate AI initiatives yield zero ROI.
Beam.ai, 2024
Overall AI project failure rate — twice that of non-AI IT projects.
RAND Corp.
Of enterprises report no EBIT impact from AI at all.
McKinsey, 2025
How it runs
How LLM development works here.
Four stages, the same shape every time — production steady-state in 4–8 weeks, scope varies but never the gates.
STEP 01
Discover
We inventory the workflows, data, and team readiness behind each use case, then rank by ROI and feasibility — including the ones we'd decline, with reasons.
Output: ranked use-case map · weeks 1–2
STEP 02
Design
Before the first prompt: a golden test set from your real data, numeric success metrics, and a benchmark of GPT-5, Claude, and Gemini tiers on your actual tasks. The engine choice comes out of that data.
Output: engine choice + eval harness
STEP 03
Build
Development happens inside your own tenant — under your SSO, logging, and retention controls. We document every data path, and full IP assignment is signed at kickoff.
Output: working system in your tenant
STEP 04
Ship & scale
Shadow mode, then pilot, then wide — with human-in-the-loop gates wherever the system acts on the world. Runbooks and the eval suite handed over with 30 days of overlap, or the pod stays on retainer.
Output: production launch + trained team
What you get
What you actually get when you hire us.
A Stanford-rooted Responsible AI lab, founded 2011, run by founder Kelvin Tran — the four commitments below are how the contract is written.
Fixed scope, one accountable lead
A dedicated pod under a single point of contact — no handoffs, no scope drift invoiced as "discovery."
Payment tied to ROI
Success metrics are defined numerically before we build, and payment is tied to hitting them.
Engine-agnostic by design
Evals before prompts, abstraction over every model API — a vendor swap is a config change.
Your people, more capable
Every engagement trains your team to run the system after we leave — Responsible AI in practice.
Track record
The track record behind the promise.
LLM systems live or die on production discipline — three named clients, all public record.
Restaurants · 200+ locations · 4+ yrs
BJ's Restaurants
Production discipline is the muscle LLM systems depend on: over 4+ years, our Aegis AI process took a 200+ location business to twice-a-week releases with zero critical defects sustained.
Sports tech · since 2012
Bridge Athletic
We shipped Bridge's first product in 2012 and carried it through twelve years of modernization — now the strength & conditioning platform used by USC, the LA Rams, and MLB and MLS teams.
Marketplace · payments · acquired
YardClub
A contractor-to-contractor marketplace for heavy construction equipment — listings, payments, transaction infrastructure — that processed $120M+ before Caterpillar acquired it in 2017.
Silicon Prime is a Stanford-rooted Responsible AI lab, founded 2011, run by founder Kelvin Tran — personally accountable for every engagement. When a use case shouldn't be built, we'll tell you that too.
Where we build
LLM systems shaped by your industry's constraints.
The constraint set changes by industry — the compliance regime, the data shapes, the cost of a wrong answer. Twenty-eight sectors we build for.
Questions buyers ask before they build.
Thirty minutes · no pitch deck
Ready to put an LLM to work — properly?
Bring the use case — we'll tell you honestly which engine fits, what it takes to build, and what it costs to run at your volume.